There are some people who are hostile to games, but in truth all games are a simplification of life and, viewed the right way, the games we like to play tell us a lot about ourselves and about what we are trying to accomplish through the simplification of life to certain elements. And, as anyone who has ever studied gamification will tell you, it is not only that life informs the games we are interested in, but similarly the popularity of games has encouraged companies to include things like badges and ranks for users and customers as a means of increasing involvement in a company’s offerings, to somewhat mixed but occasionally entertaining results.

It is not too surprising that we would want our games to simplify life. A great deal of what is done in the contemporary world involves simplifying the world in order to better understand it. This offers both opportunity and danger. To the extent that we model and simplify life, we need to be aware that we are simplifying and not to confuse the model with the real world that we are modeling. We can gain insights into the real world from models, we can sharpen our thinking and develop reflexes and pattern recognition that can help us in the real world, but the game or model is always far simpler than the real world that it is trying to mimic. If we think that the model is reality, we can see things in reality that do not necessarily exist because of the way that they have been modeled, and that is something that can be a real problem.

There are times, though, where simplification of reality can be of immense benefit in understanding realty. One example of this is chess, where the simplification is so obviously not realistic that one can focus on the spatial relationships involved on the board and hone one’s pattern recognition skills to a high degree without being in fear of saying something about reality that does not work. Another example, surprisingly enough, are the basic origin of life programs that demonstrate (perhaps unintentionally) that life requires design in order to work even in drastically oversimplified situations. Perhaps for this reason such programs have become far less popular as of late, because the work to model situations that are close to realistic demonstrates the high demands of programming and information content involved in creating the context in which behavior can occur.

A big part of the question is, are the insights we gain coming from the ways in which a model captures some aspect of reality or is it from the way that the model is designed itself. One would be grossly out of luck if one sought to master military tactics merely from using a chessboard, because it might lead one to only look at a narrow grid when it came to dealing with the combination of forces and might lead one to underestimate the importance of securing one’s flanks and not merely assuming that the opponent was going to match one’s own width. Yet one can profitably learn from how chess progresses certain tactical principles that are of use in the world concerning the use of one’s resources, positional advantages that make it hard for others to do anything against you, and the like. The question, as it is often is, is how one is using something to gain insight about something else, and is one learning the right kind of lessons about patterns that can be found across the board?